Super-resolution reconstruction method of remote sensing images based on content-aware deep learning network

A deep learning network and super-resolution reconstruction technology, applied in the field of image processing, can solve the problems of blurring and under-fitting of super-resolution results, reduce the naturalness and fidelity of reconstructed images, and improve the universality of application. , Overcome over-fitting and under-fitting, and improve the effect of accuracy
CN107194872BActive Publication Date: 2019-08-20WUHAN UNIV
4 Cites 0 Cited by

Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Patents(China)
Current Assignee / Owner
WUHAN UNIV
Publication Date
2019-08-20

Smart Images

  • Figure 1
    Figure 1
  • Figure 2
    Figure 2
  • Figure 3
    Figure 3
Patent Text Reader

Abstract

The invention discloses a content awareness deep learning network-based remote sensing image super-resolution reconstruction method. A comprehensive measurement index and a calculation method for content complexity of images are proposed; based on this, the sample images are classified by the content complexity; deep GAN models with low, medium and high complexity are built and trained; and according to the content complexity of the to-be-classified super-resolution input images, corresponding networks are selected for performing reconstruction. In order to improve learning performance of a GAN, an optimized loss function definition is given. The method overcomes the over-fitting and under-fitting contradiction ubiquitous in machine learning-based super-resolution reconstruction, and effectively improves the super-resolution reconstruction precision of the remote sensing images.
Need to check novelty before this filing date? Find Prior Art

Description

technical field

[0001] The invention belongs to the technical field of image processing, and relates to an image super-resolution reconstruction method, in particular to a remote sensing image super-resolution reconstruction method based on a content-aware deep learning network. Background technique

[0002] Remote sensing images with high spatial resolution can describe the ground objects more finely and provide rich detail information. Therefore, people often hope to obtain images with high spatial resolution. With the rapid development of space detection theory and technology, remote sensing images with meter-level or even sub-meter-level spatial resolution (such as IKNOS and QuickBird) have been gradually applied, but their temporal resolution is generally relatively low. On the contrary, some sensors with lower spatial resolution (such as MODIS) have high temporal resolution, and they can acquire a large-scale remote sensing image in a short time. If high-spatial-resol...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More